Visible, Editable User Context
If context is going to shape a response, I want the interface to show what context was used and give people a way to correct it.
A recurring question for me is how software stays understandable, especially when an LLM is involved. If a system gives a personalized answer, it is reasonable to wonder why it said that and what it thinks it knows about you.
So I made the user context passed along with each request visible, editable, and deletable item by item. The goal is simple: show the facts and guesses that may shape the response, then let the user correct them.
This became especially timely when OpenAI introduced memory sources for ChatGPT. That update gave people more visibility into the context behind a personalized response, along with controls to delete or correct stale information.
Even after a few interactions, the prototype had collected a mix of facts and guesses. Separating those categories changes the feel of the system: it becomes easier to inspect, challenge, and trust.
